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cturkieh

France Data MCP

rpps_search_by_name

Read-onlyIdempotent

Find healthcare professionals by name with accent- and typo-tolerant trigram matching. Filter by first name and department, and assess result reliability via match score and geo precision.

Instructions

Trouve un PS par identité (matching trigram tolérant aux accents/typos). Usage : "Dr Martin à Paris" → nom: "Martin", departement: "75". Nom obligatoire ; prenom et departement affinent.

Tri par match_score ∈ [0..1] décroissant (score trigram pg_trgm). Un score <0.5 = homonymie partielle à confirmer côté caller. Sans departement, des homonymes exacts ("Pierre Martin") ont TOUS le même score ~1.0 et ne sont pas départagés — toujours filtrer par dept ou prénom sur un nom commun.

truncated: true = d'autres résultats existent (restreindre, ne pas parcourir).

Chaque résultat géolocalisé porte geo_precision ∈ {"adresse", "etablissement_finess", "centroide_commune"} — lire ce champ pour évaluer la fiabilité des coords (précise BAN/FINESS au m près vs centroïde commune ~3 km, non discriminant intra-commune).

Catégorie par défaut : Civil (C, ~97 % — libéraux, salariés privés, hospitaliers contractuels). Opt-in : include_agents_publics: true ajoute Agents publics (M, ~0,3 % — PH titulaires, ARS, CNAM, Éducation nationale, PMI, militaires SSA) ; include_etudiants: true ajoute Étudiants (E, ~2,5 % — internes, externes, élèves IDE/SF). Réf : https://mos.esante.gouv.fr/NOS/TRE_R09-CategorieProfessionnelle/.

Source : Annuaire Santé, Agence du Numérique en Santé (ANS) — Licence Ouverte v2.0

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nomYesNom de famille (non vide).
prenomNoPrénom du PS.
departementNoCode département INSEE (ex: '75', '2A', '2B', '971'). Métropole 2 caractères (Corse '2A'/'2B', pas '20'), DOM/COM 3 caractères.
include_etudiantsNo
include_agents_publicsNo
limitNoNombre max de résultats (1-500, défaut 100).
include_freshnessNoSi true, ajoute un champ `data_freshness` au payload (dans `query_metadata` si présent, sinon à la racine) listant la dernière ingestion réussie par source (FINESS, Ameli, RPPS, CDS) avec `staleness_days`. Opt-in pour ne pas alourdir les payloads par défaut. Cache 5min côté serveur — coût négligeable.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNombre d'entrées retournées dans `results` (post-troncature).
totalNoEffectif réel avant troncature. Présent sur les tools de nomenclature paginés (lister_*) : `count` = échantillon, `total` = total réel, re-appeler avec un `limit` supérieur si `truncated`.
truncatedNotrue si le total réel dépasse `limit` (re-paginer via `offset` si supporté, ou augmenter `limit` sur les lister_*). Optional sur les tools de listing exhaustif (lister_*).
resultsYesEntrées métier (shape spécifique au tool, cf. description du tool).
query_metadataNoMetadata de la query (radius_km, departement, filtres appliqués, …).
freshnessNoFraîcheur des sources (présent si `include_freshness: true`).
perimetreNoLentille de la source : ce que le comptage inclut/exclut. Lire `completeness_note` et la restituer au lecteur final.
activite_hebergeeNoCompte juxtaposé des sites hébergeant l'activité correspondant à la famille filtrée, sous une autre catégorie FINESS. Distinct du `count` principal — lire `note` pour comprendre la sémantique et ne JAMAIS additionner les deux comptes sans préciser leur nature.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint, destructiveHint, idempotentHint, and openWorldHint are all true/false appropriately. The description adds crucial details: match_score threshold (<0.5 indicates partial homonymy), truncated flag meaning more results exist, geo_precision levels with distance implications, and a 5-minute server cache for freshness. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear paragraphs and front-loaded with essential information. Every sentence adds value, though it is somewhat lengthy. However, given the complexity of the tool (7 parameters, multiple behavioral nuances), the length is justified and well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is exhaustive given the tool's complexity. It covers purpose, parameters, scoring, precision, categories, source, and license. The output schema is implied through description of fields (match_score, truncated, geo_precision, data_freshness). The context signals indicate high parameter count and schema coverage, and the description fills all gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 71%, but the description significantly enhances understanding. It clarifies that nom is mandatory, explains the departement format with examples (75, 2A, 2B, 971), details the category parameters (including relative percentages), and provides a rationale for include_freshness (optional to avoid payload bloat). This goes well beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool finds a health professional by identity using trigram matching tolerant to accents and typos. It gives a concrete usage example ('Dr Martin à Paris' -> nom: 'Martin', departement: '75') and distinguishes itself from siblings by focusing on name-based search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit instructions: nom is mandatory, prenom and departement refine results, and warns about homonyms when departement is omitted. It explains the default category (Civil) and how to opt-in for agents publics and étudiants, including a reference URL. It also tells when to use the truncated flag and how to interpret geo_precision.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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